AsianScientist (May 2, 2017) – Researchers from Osaka University have designed a tool that uses machine learning to model personal sleep patterns using sounds made during sleep. The results have been published in Trends in Applied Knowledge-Based Systems and Data Science.
Despite spending at least one quarter to one third a day sleeping, good sleep can elude many people, and the diagnosis and treatment of sleep disorders remains primitive.
Patients with sleep disorders are often evaluated by polysomnography (PSG), which measures an assortment of activities during sleep, including brain activity, eye movement and heart rhythms. However, PSGs are ineffective because they take the patient out of his natural sleeping environment.
“Our environment influences how we sleep. We should not expect the same patterns sleeping at a hospital or sleeping at home,” said Associate Professor Ken-ichi Fukui.
Fukui and other researchers at the Institute of Scientific and Industrial Research, Osaka University, collaborated with researchers at the Dental School. The sounds of sleeping dental students were recorded using smartphones placed at their bedsides. Fukui then used a novel machine learning algorithm prepared by his group to analyze the sounds and compare them with PSG data taken from the same sleeping students.
“We modified self-organizing map (SOM) algorithms to visualize the dynamics of sleep,” he said.
The SOMs extracted very obvious sleeping patterns, as subjects showed clear changes in their sounds with their sleep stage. Snoring was evident during deep sleep periods, whereas some of the sleepers tended to grind their teeth during light sleep periods.
“Visualization by SOM makes it very easy to detect abnormal patterns in sleeping,” added Fukui.
Associating sleep sounds with sleep patterns provides a whole new prospect of sleep diagnostics.
“There are lots of devices that assess sleep,” Fukui noted, “but none are scientifically proven, and they usually require you wear the device,” which he adds discourages their use.
Fukui expects that apps for smartphones and tablets can be used to record sleep sounds and convert the information into recommended adjustments to the home, such as lighting or room temperature, for better sleep.
The article can be found at: Wu et al. (2016) Sleep Pattern Discovery via Visualizing Cluster Dynamics of Sound Data.
———
Source: Osaka University; Photo: Pexels.
Disclaimer: This article does not necessarily reflect the views of AsianScientist or its staff.
